---
title: AwaDB
---

>[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.

This notebook explains how to use `AwaEmbeddings` in LangChain.

```python
# pip install awadb
```

## import the library

```python
from langchain_community.embeddings import AwaEmbeddings
```

```python
Embedding = AwaEmbeddings()
```

# Set embedding model

Users can use `Embedding.set_model()` to specify the embedding model. \
The input of this function is a string which represents the model's name. \
The list of currently supported models can be obtained [here](https://github.com/awa-ai/awadb) \ \

The **default model** is `all-mpnet-base-v2`, it can be used without setting.

```python
text = "our embedding test"

Embedding.set_model("all-mpnet-base-v2")
```

```python
res_query = Embedding.embed_query("The test information")
res_document = Embedding.embed_documents(["test1", "another test"])
```
